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Prediction of IgG1 aggregation in solution.

Frida Ojala1, Marcus Degerman, Thomas Budde Hansen

  • 1Department of Chemical Engineering, Lund University, Lund, Sweden.

Biotechnology Journal
|April 25, 2014
PubMed
Summary
This summary is machine-generated.

Monoclonal antibody aggregation, forming dimers, can be predicted using a new stoichiometric reaction model. This model helps optimize storage conditions to maintain antibody stability and prevent immunogenic responses.

Keywords:
AggregationAntibodiesBiopharmaceuticalsReversible reaction modelSolution

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Area of Science:

  • Biopharmaceutical development
  • Protein aggregation kinetics
  • Antibody stability

Background:

  • Monoclonal antibody (mAb) aggregation is a critical concern in biopharmaceuticals due to potential immunogenicity.
  • Understanding and predicting antibody aggregation is essential for ensuring therapeutic efficacy and patient safety.

Purpose of the Study:

  • To develop a stoichiometric reaction model to predict dimer formation in stored antibody solutions.
  • To investigate the influence of pH, salt concentration, and protein concentration on antibody aggregation kinetics.

Main Methods:

  • Incubation of IgG1 under varying pH (4.5-5.5), salt concentrations (100-600 mmol/kg), and protein concentrations (10.6-26.3 g/L).
  • Analysis of samples over time (4 h to 7.6 days) using size-exclusion chromatography (SEC).
  • Development of a stoichiometric reaction model based on concentration-time data and SEC analysis.

Main Results:

  • Observed formation of dimers from monomers, with no higher-order aggregates detected.
  • Demonstrated reversibility of dimerization upon sample dilution.
  • The stoichiometric model successfully predicted antibody dimer formation.
  • Identified salt concentration as a key factor influencing the equilibrium constant and pH as a factor affecting the kinetic constant.

Conclusions:

  • The developed stoichiometric model accurately predicts dimer formation in monoclonal antibody solutions.
  • The study provides insights into optimizing storage conditions (pH and salt concentration) to minimize antibody aggregation.
  • Predictive modeling of aggregation can enhance the stability and safety of biopharmaceutical products.